Overview

Dataset statistics

Number of variables12
Number of observations1119
Missing cells0
Missing cells (%)0.0%
Duplicate rows123
Duplicate rows (%)11.0%
Total size in memory105.0 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

Dataset has 123 (11.0%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
pH is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
citric acid has 94 (8.4%) zerosZeros

Reproduction

Analysis started2023-04-13 21:03:50.799827
Analysis finished2023-04-13 21:03:59.139986
Duration8.34 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct91
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3095621
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.180853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.19
Q17.1
median7.9
Q39.2
95-th percentile11.6
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7138987
Coefficient of variation (CV)0.2062562
Kurtosis1.3115549
Mean8.3095621
Median Absolute Deviation (MAD)0.9
Skewness0.9941776
Sum9298.4
Variance2.9374487
MonotonicityNot monotonic
2023-04-13T17:03:59.318565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 44
 
3.9%
7.1 42
 
3.8%
7.7 41
 
3.7%
7.5 39
 
3.5%
7.8 37
 
3.3%
6.9 33
 
2.9%
8 32
 
2.9%
7.6 32
 
2.9%
7 32
 
2.9%
7.4 31
 
2.8%
Other values (81) 756
67.6%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 4
 
0.4%
5.1 3
 
0.3%
5.2 6
0.5%
5.3 4
 
0.4%
5.4 2
 
0.2%
5.6 11
1.0%
5.7 1
 
0.1%
ValueCountFrequency (%)
15.9 1
 
0.1%
15.6 2
0.2%
15.5 1
 
0.1%
15 1
 
0.1%
13.8 1
 
0.1%
13.7 1
 
0.1%
13.4 1
 
0.1%
13.3 3
0.3%
13.2 2
0.2%
13 3
0.3%

volatile acidity
Real number (ℝ)

Distinct134
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53313226
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.376799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.28
Q10.4
median0.52
Q30.64
95-th percentile0.8505
Maximum1.58
Range1.46
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.18202166
Coefficient of variation (CV)0.34141933
Kurtosis1.5044412
Mean0.53313226
Median Absolute Deviation (MAD)0.12
Skewness0.75587117
Sum596.575
Variance0.033131884
MonotonicityNot monotonic
2023-04-13T17:03:59.431283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 34
 
3.0%
0.43 31
 
2.8%
0.6 31
 
2.8%
0.58 29
 
2.6%
0.59 28
 
2.5%
0.41 27
 
2.4%
0.36 26
 
2.3%
0.52 25
 
2.2%
0.38 25
 
2.2%
0.4 25
 
2.2%
Other values (124) 838
74.9%
ValueCountFrequency (%)
0.12 1
 
0.1%
0.16 1
 
0.1%
0.18 7
0.6%
0.19 1
 
0.1%
0.2 3
0.3%
0.21 3
0.3%
0.22 4
0.4%
0.23 4
0.4%
0.24 7
0.6%
0.25 6
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.2%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.3%
1.025 1
 
0.1%
1.02 3
0.3%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27025022
Minimum0
Maximum1
Zeros94
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.492655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.26
Q30.43
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.19549151
Coefficient of variation (CV)0.72337226
Kurtosis-0.7583068
Mean0.27025022
Median Absolute Deviation (MAD)0.17
Skewness0.32306222
Sum302.41
Variance0.038216932
MonotonicityNot monotonic
2023-04-13T17:03:59.551028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
8.4%
0.49 51
 
4.6%
0.02 35
 
3.1%
0.24 33
 
2.9%
0.26 28
 
2.5%
0.01 27
 
2.4%
0.1 25
 
2.2%
0.32 25
 
2.2%
0.21 25
 
2.2%
0.03 24
 
2.1%
Other values (69) 752
67.2%
ValueCountFrequency (%)
0 94
8.4%
0.01 27
 
2.4%
0.02 35
 
3.1%
0.03 24
 
2.1%
0.04 20
 
1.8%
0.05 13
 
1.2%
0.06 15
 
1.3%
0.07 16
 
1.4%
0.08 19
 
1.7%
0.09 20
 
1.8%
ValueCountFrequency (%)
1 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.3%
0.75 1
 
0.1%
0.74 3
0.3%
0.73 3
0.3%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 1
 
0.1%
0.69 1
 
0.1%

residual sugar
Real number (ℝ)

Distinct79
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5483021
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.606912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.6
95-th percentile5.155
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.4277303
Coefficient of variation (CV)0.56026731
Kurtosis28.130263
Mean2.5483021
Median Absolute Deviation (MAD)0.3
Skewness4.5112564
Sum2851.55
Variance2.0384139
MonotonicityNot monotonic
2023-04-13T17:03:59.662245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 106
 
9.5%
2.2 96
 
8.6%
2.1 93
 
8.3%
1.8 90
 
8.0%
1.9 80
 
7.1%
2.3 74
 
6.6%
2.5 61
 
5.5%
2.4 60
 
5.4%
2.6 56
 
5.0%
1.7 51
 
4.6%
Other values (69) 352
31.5%
ValueCountFrequency (%)
0.9 2
 
0.2%
1.2 4
 
0.4%
1.3 3
 
0.3%
1.4 26
 
2.3%
1.5 24
 
2.1%
1.6 38
3.4%
1.7 51
4.6%
1.75 2
 
0.2%
1.8 90
8.0%
1.9 80
7.1%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 1
0.1%
13.9 1
0.1%
13.8 2
0.2%
12.9 1
0.1%
11 1
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%
8.8 1
0.1%

chlorides
Real number (ℝ)

Distinct136
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087711349
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.719573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.071
median0.08
Q30.09
95-th percentile0.1284
Maximum0.611
Range0.599
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.047142931
Coefficient of variation (CV)0.53747812
Kurtosis45.20152
Mean0.087711349
Median Absolute Deviation (MAD)0.01
Skewness5.8357464
Sum98.149
Variance0.002222456
MonotonicityNot monotonic
2023-04-13T17:03:59.774433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 53
 
4.7%
0.077 35
 
3.1%
0.074 35
 
3.1%
0.075 34
 
3.0%
0.076 34
 
3.0%
0.082 33
 
2.9%
0.084 33
 
2.9%
0.079 32
 
2.9%
0.071 31
 
2.8%
0.078 30
 
2.7%
Other values (126) 769
68.7%
ValueCountFrequency (%)
0.012 2
0.2%
0.034 1
 
0.1%
0.038 2
0.2%
0.039 2
0.2%
0.041 2
0.2%
0.042 2
0.2%
0.043 1
 
0.1%
0.044 1
 
0.1%
0.045 4
0.4%
0.046 3
0.3%
ValueCountFrequency (%)
0.611 1
0.1%
0.61 1
0.1%
0.467 1
0.1%
0.415 2
0.2%
0.414 2
0.2%
0.387 1
0.1%
0.368 1
0.1%
0.36 1
0.1%
0.358 1
0.1%
0.343 1
0.1%

free sulfur dioxide
Real number (ℝ)

Distinct55
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.920465
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.830868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum68
Range67
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.273166
Coefficient of variation (CV)0.64528055
Kurtosis1.8651803
Mean15.920465
Median Absolute Deviation (MAD)7
Skewness1.1899155
Sum17815
Variance105.53794
MonotonicityNot monotonic
2023-04-13T17:03:59.886750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 95
 
8.5%
5 73
 
6.5%
12 58
 
5.2%
10 55
 
4.9%
15 55
 
4.9%
7 54
 
4.8%
17 49
 
4.4%
9 42
 
3.8%
16 41
 
3.7%
13 40
 
3.6%
Other values (45) 557
49.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 36
 
3.2%
4 23
 
2.1%
5 73
6.5%
5.5 1
 
0.1%
6 95
8.5%
7 54
4.8%
8 36
 
3.2%
9 42
3.8%
ValueCountFrequency (%)
68 2
0.2%
66 1
0.1%
57 1
0.1%
55 1
0.1%
53 1
0.1%
52 1
0.1%
51 1
0.1%
50 2
0.2%
48 2
0.2%
45 2
0.2%

total sulfur dioxide
Real number (ℝ)

Distinct137
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.966488
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:03:59.945037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q364
95-th percentile114.1
Maximum289
Range283
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.036693
Coefficient of variation (CV)0.7034099
Kurtosis2.8910308
Mean46.966488
Median Absolute Deviation (MAD)18
Skewness1.3820309
Sum52555.5
Variance1091.4231
MonotonicityNot monotonic
2023-04-13T17:03:59.999336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 32
 
2.9%
20 27
 
2.4%
23 26
 
2.3%
14 25
 
2.2%
24 25
 
2.2%
18 25
 
2.2%
38 24
 
2.1%
16 22
 
2.0%
19 21
 
1.9%
10 20
 
1.8%
Other values (127) 872
77.9%
ValueCountFrequency (%)
6 2
 
0.2%
7 3
 
0.3%
8 11
1.0%
9 10
 
0.9%
10 20
1.8%
11 16
1.4%
12 17
1.5%
13 20
1.8%
14 25
2.2%
15 19
1.7%
ValueCountFrequency (%)
289 1
 
0.1%
152 1
 
0.1%
151 2
0.2%
149 1
 
0.1%
147 2
0.2%
145 3
0.3%
144 3
0.3%
143 1
 
0.1%
142 1
 
0.1%
141 3
0.3%

density
Real number (ℝ)

Distinct360
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.996778
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:04:00.061640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.99371
Q10.995685
median0.9968
Q30.997845
95-th percentile0.9998
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.00216

Descriptive statistics

Standard deviation0.0018402855
Coefficient of variation (CV)0.0018462341
Kurtosis1.2051646
Mean0.996778
Median Absolute Deviation (MAD)0.00108
Skewness0.023790919
Sum1115.3946
Variance3.3866507 × 10-6
MonotonicityNot monotonic
2023-04-13T17:04:00.123948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9968 25
 
2.2%
0.9976 25
 
2.2%
0.9978 23
 
2.1%
0.9972 23
 
2.1%
0.998 20
 
1.8%
0.9964 18
 
1.6%
0.997 18
 
1.6%
0.9994 18
 
1.6%
0.9962 17
 
1.5%
0.9982 16
 
1.4%
Other values (350) 916
81.9%
ValueCountFrequency (%)
0.99007 2
0.2%
0.9902 1
0.1%
0.99064 2
0.2%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.2%
0.99182 2
0.2%
0.9921 1
0.1%
0.9922 2
0.2%
ValueCountFrequency (%)
1.00369 1
0.1%
1.0032 1
0.1%
1.00315 2
0.2%
1.00289 1
0.1%
1.0026 2
0.2%
1.00242 2
0.2%
1.0022 1
0.1%
1.0021 2
0.2%
1.0015 1
0.1%
1.0014 2
0.2%

pH
Real number (ℝ)

Distinct85
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3142717
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:04:00.258027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.07
Q13.22
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.15397994
Coefficient of variation (CV)0.046459662
Kurtosis0.81346726
Mean3.3142717
Median Absolute Deviation (MAD)0.09
Skewness0.19816207
Sum3708.67
Variance0.023709822
MonotonicityNot monotonic
2023-04-13T17:04:00.317343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 46
 
4.1%
3.26 41
 
3.7%
3.36 38
 
3.4%
3.29 35
 
3.1%
3.39 34
 
3.0%
3.38 34
 
3.0%
3.32 29
 
2.6%
3.28 29
 
2.6%
3.22 28
 
2.5%
3.31 28
 
2.5%
Other values (75) 777
69.4%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.88 2
0.2%
2.89 3
0.3%
2.9 1
 
0.1%
2.92 3
0.3%
2.93 2
0.2%
2.94 3
0.3%
2.95 1
 
0.1%
2.98 3
0.3%
2.99 2
0.2%
ValueCountFrequency (%)
4.01 1
 
0.1%
3.9 2
 
0.2%
3.85 1
 
0.1%
3.78 1
 
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.71 4
0.4%
3.69 4
0.4%
3.68 5
0.4%
3.67 1
 
0.1%

sulphates
Real number (ℝ)

Distinct91
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65882038
Minimum0.37
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:04:00.377146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.93
Maximum2
Range1.63
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.17224198
Coefficient of variation (CV)0.26143998
Kurtosis12.426184
Mean0.65882038
Median Absolute Deviation (MAD)0.08
Skewness2.5535875
Sum737.22
Variance0.029667301
MonotonicityNot monotonic
2023-04-13T17:04:00.433483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58 50
 
4.5%
0.62 46
 
4.1%
0.54 45
 
4.0%
0.6 44
 
3.9%
0.56 41
 
3.7%
0.57 39
 
3.5%
0.55 38
 
3.4%
0.64 37
 
3.3%
0.53 36
 
3.2%
0.63 34
 
3.0%
Other values (81) 709
63.4%
ValueCountFrequency (%)
0.37 1
 
0.1%
0.39 3
 
0.3%
0.4 3
 
0.3%
0.42 4
 
0.4%
0.43 6
 
0.5%
0.44 13
1.2%
0.45 6
 
0.5%
0.46 13
1.2%
0.47 13
1.2%
0.48 24
2.1%
ValueCountFrequency (%)
2 1
0.1%
1.98 1
0.1%
1.95 1
0.1%
1.62 1
0.1%
1.61 1
0.1%
1.59 1
0.1%
1.56 1
0.1%
1.36 1
0.1%
1.34 1
0.1%
1.33 1
0.1%

alcohol
Real number (ℝ)

Distinct60
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.417337
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:04:00.492286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0597513
Coefficient of variation (CV)0.10172958
Kurtosis0.36204336
Mean10.417337
Median Absolute Deviation (MAD)0.7
Skewness0.91547626
Sum11657
Variance1.1230727
MonotonicityNot monotonic
2023-04-13T17:04:00.548567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 102
 
9.1%
9.4 69
 
6.2%
10.5 54
 
4.8%
9.8 53
 
4.7%
9.2 50
 
4.5%
9.6 47
 
4.2%
10 46
 
4.1%
11 44
 
3.9%
9.7 40
 
3.6%
10.9 39
 
3.5%
Other values (50) 575
51.4%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.8 2
 
0.2%
9 19
 
1.7%
9.05 1
 
0.1%
9.1 17
 
1.5%
9.2 50
4.5%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
9.3 39
3.5%
9.4 69
6.2%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 4
0.4%
13.6 4
0.4%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.3%
13.3 2
 
0.2%
13.1 1
 
0.1%
13 3
0.3%
12.9 6
0.5%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6219839
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-04-13T17:04:00.597344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8119739
Coefficient of variation (CV)0.14442836
Kurtosis0.37976123
Mean5.6219839
Median Absolute Deviation (MAD)1
Skewness0.18972946
Sum6291
Variance0.65930161
MonotonicityNot monotonic
2023-04-13T17:04:00.636853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 486
43.4%
6 438
39.1%
7 138
 
12.3%
4 36
 
3.2%
8 12
 
1.1%
3 9
 
0.8%
ValueCountFrequency (%)
3 9
 
0.8%
4 36
 
3.2%
5 486
43.4%
6 438
39.1%
7 138
 
12.3%
8 12
 
1.1%
ValueCountFrequency (%)
8 12
 
1.1%
7 138
 
12.3%
6 438
39.1%
5 486
43.4%
4 36
 
3.2%
3 9
 
0.8%

Interactions

2023-04-13T17:03:58.394261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:50.893516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.586688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.239368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.936872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.589890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.299277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.944820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.658084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.315088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.025940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.685519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.442627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:50.943858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.637039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.288210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.990700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.642234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.350627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.995659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.710380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.365918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.078881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.735792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.494450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.071951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.693851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.341553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.045038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.697058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.404979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.049998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.765701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.419201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.134219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.789612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.542814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.119317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.747192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.472674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.096874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.823694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.459796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.179325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.818692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.549340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.188039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.919194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.592648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.173138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.801592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.525027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.150218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.878510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.512150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.231680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.873513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.602175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.243385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.972018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.643008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.223497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.858338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.575857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.205571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.929868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.566967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.284499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.928675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.654088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.298209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.023914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.695839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.276764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.914777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.628211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.258394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.984685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.620317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.339842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.984081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.707430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.355531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.075741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.745198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.326129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.967605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.681035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.311745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.035047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.673141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.392671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.038428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.760250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.409865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.128097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.797030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.379250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.024924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.733494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.371542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.088876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.728486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.447006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.096242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.816586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.467675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.185907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.846386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.431067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.077747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.783327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.426894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.141260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.781309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.499837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.149583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.868416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.521540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.237265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.902205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.485505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.133270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.837678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.484701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.197082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.840641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.554187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.206922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.922762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.576353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.292081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.952559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:51.536853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.187020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:52.887516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:53.537056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.249434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:54.893472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:55.606520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.262734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:56.973589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:57.630703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-13T17:03:58.343431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-13T17:04:00.678712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.2630.6500.2050.244-0.163-0.0810.604-0.7080.188-0.0500.083
volatile acidity-0.2631.000-0.6070.0290.1790.0200.0910.0190.219-0.349-0.236-0.373
citric acid0.650-0.6071.0000.1890.121-0.0690.0260.356-0.5380.3200.1010.183
residual sugar0.2050.0290.1891.0000.1930.0460.1190.429-0.0800.0350.1290.035
chlorides0.2440.1790.1210.1931.000-0.0110.1150.381-0.232-0.035-0.268-0.202
free sulfur dioxide-0.1630.020-0.0690.046-0.0111.0000.798-0.0300.1040.036-0.106-0.065
total sulfur dioxide-0.0810.0910.0260.1190.1150.7981.0000.132-0.018-0.018-0.278-0.210
density0.6040.0190.3560.4290.381-0.0300.1321.000-0.2980.142-0.435-0.196
pH-0.7080.219-0.538-0.080-0.2320.104-0.018-0.2981.000-0.0570.169-0.008
sulphates0.188-0.3490.3200.035-0.0350.036-0.0180.142-0.0571.0000.2430.389
alcohol-0.050-0.2360.1010.129-0.268-0.106-0.278-0.4350.1690.2431.0000.500
quality0.083-0.3730.1830.035-0.202-0.065-0.210-0.196-0.0080.3890.5001.000

Missing values

2023-04-13T17:03:59.023849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-13T17:03:59.102598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
08.60.2200.361.90.06453.077.00.996043.470.8711.07
112.50.4600.632.00.0716.015.00.998802.990.8710.25
27.20.5400.272.60.08412.078.00.996403.390.7111.05
36.40.6700.082.10.04519.048.00.994903.490.4911.46
47.50.5800.142.20.07727.060.00.996303.280.599.85
57.80.5800.132.10.10217.036.00.994403.240.5311.26
66.60.8150.022.70.07217.034.00.995503.580.8912.37
76.30.5700.282.10.04813.049.00.993743.410.6012.85
87.20.6950.132.00.07612.020.00.995463.290.5410.15
99.50.5900.442.30.07121.068.00.999203.460.639.55
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
11098.10.7300.002.50.08112.024.00.997983.380.469.64
111010.30.5000.422.00.06921.051.00.998203.160.7211.56
11118.80.5500.042.20.11914.056.00.996203.210.6010.96
11126.40.3900.333.30.04612.053.00.992943.360.6212.26
11139.40.4000.472.50.0876.020.00.997723.150.5010.55
11149.10.6000.001.90.0585.010.00.997703.180.6310.46
11158.20.6350.102.10.07325.060.00.996383.290.7510.96
11167.20.6200.062.70.07715.085.00.997463.510.549.55
11177.90.2000.351.70.0547.015.00.994583.320.8011.97
11185.80.2900.261.70.0633.011.00.991503.390.5413.56

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
217.00.6900.072.50.09115.021.00.995723.380.6011.363
357.20.6950.132.00.07612.020.00.995463.290.5410.153
467.50.5100.021.70.08413.031.00.995383.360.5410.563
798.30.6500.102.90.08917.040.00.998033.290.559.553
979.30.3600.391.50.08041.055.00.996523.470.7310.963
1069.90.5400.452.30.07116.040.00.999103.390.629.453
05.20.3400.001.80.05027.063.00.991603.680.7914.062
15.60.5000.092.30.04917.099.00.993703.630.6313.052
25.60.6600.002.20.0873.011.00.993783.710.6312.872
36.00.5000.001.40.05715.026.00.994483.360.459.552